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   "source": [
    "# Bayesian Regression Using NumPyro\n",
    "\n",
    "In this tutorial, we will explore how to do bayesian regression in NumPyro, using a simple example adapted from Statistical Rethinking [[1](#References)]. In particular, we would like to explore the following:\n",
    "\n",
    " - Write a simple model using the `sample` NumPyro primitive.\n",
    " - Run inference using MCMC in NumPyro, in particular, using the No U-Turn Sampler (NUTS) to get a posterior distribution over our regression parameters of interest.\n",
    " - Learn about inference utilities such as `Predictive` and `log_likelihood`.\n",
    " - Learn how we can use effect-handlers in NumPyro to generate execution traces from the model, condition on sample statements, seed models with RNG seeds, etc., and use this to implement various utilities that will be useful for MCMC. e.g. computing model log likelihood, generating empirical distribution over the posterior predictive, etc.\n",
    "\n",
    "## Tutorial Outline:\n",
    "\n",
    "1. [Dataset](#Dataset)\n",
    "2. [Regression Model to Predict Divorce Rate](#Regression-Model-to-Predict-Divorce-Rate)\n",
    "    - [Model-1: Predictor-Marriage Rate](#Model-1:-Predictor---Marriage-Rate)\n",
    "       - [Posterior Distribution over the Regression Parameters](#Posterior-Distribution-over-the-Regression-Parameters)\n",
    "       - [Prior Predictive Distribution](#Prior-Predictive-Distribution)\n",
    "       - [Posterior Predictive Distribution](#Posterior-Predictive-Distribution)\n",
    "       - [Predictive Utility With Effect Handlers](#Predictive-Utility-With-Effect-Handlers)\n",
    "       - [Posterior Predictive Density](#Posterior-Predictive-Density)\n",
    "    - [Model-2: Predictor-Median Age of Marriage](#Model-2:-Predictor---Median-Age-of-Marriage)\n",
    "    - [Model-3: Predictor-Marriage Rate and Median Age of Marriage](#Model-3:-Predictor---Marriage-Rate-and-Median-Age-of-Marriage)\n",
    "    - [Divorce Rate Residuals by State](#Divorce-Rate-Residuals-by-State)\n",
    "3. [Regression Model with Measurement Error](#Regression-Model-with-Measurement-Error)\n",
    "    - [Effect of Incorporating Measurement Noise on Residuals](#Effect-of-Incorporating-Measurement-Noise-on-Residuals)\n",
    "4. [References](#References)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T17:12:15.637112Z",
     "start_time": "2024-07-29T17:12:05.726504Z"
    },
    "id": "FlhcyvtqN7l1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T17:16:21.100577Z",
     "start_time": "2024-07-29T17:16:20.871186Z"
    },
    "id": "B_9Gru7DN7l3"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from IPython.display import set_matplotlib_formats\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from jax import random, vmap\n",
    "import jax.numpy as jnp\n",
    "from jax.scipy.special import logsumexp\n",
    "\n",
    "import numpyro\n",
    "from numpyro import handlers\n",
    "from numpyro.diagnostics import hpdi\n",
    "import numpyro.distributions as dist\n",
    "from numpyro.infer import MCMC, NUTS\n",
    "\n",
    "plt.style.use(\"bmh\")\n",
    "if \"NUMPYRO_SPHINXBUILD\" in os.environ:\n",
    "    set_matplotlib_formats(\"svg\")\n",
    "\n",
    "assert numpyro.__version__.startswith(\"0.19.0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YfP23JA7N7l3"
   },
   "source": [
    "## Dataset\n",
    "\n",
    "For this example, we will use the `WaffleDivorce` dataset from Chapter 05, Statistical Rethinking [[1](#References)]. The dataset contains divorce rates in each of the 50 states in the USA, along with predictors such as population, median age of marriage, whether it is a Southern state and, curiously, number of Waffle Houses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T17:16:22.125032Z",
     "start_time": "2024-07-29T17:16:22.058625Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "KsCe9ruUN7l4",
    "outputId": "f26f9596-9f21-46d6-ec58-33dfc92b58a0"
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   "outputs": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Loc</th>\n",
       "      <th>Population</th>\n",
       "      <th>MedianAgeMarriage</th>\n",
       "      <th>Marriage</th>\n",
       "      <th>Marriage SE</th>\n",
       "      <th>Divorce</th>\n",
       "      <th>Divorce SE</th>\n",
       "      <th>WaffleHouses</th>\n",
       "      <th>South</th>\n",
       "      <th>Slaves1860</th>\n",
       "      <th>Population1860</th>\n",
       "      <th>PropSlaves1860</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>4.78</td>\n",
       "      <td>25.3</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1.27</td>\n",
       "      <td>12.7</td>\n",
       "      <td>0.79</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>435080</td>\n",
       "      <td>964201</td>\n",
       "      <td>0.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>0.71</td>\n",
       "      <td>25.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.93</td>\n",
       "      <td>12.5</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>6.33</td>\n",
       "      <td>25.8</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.98</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.74</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>2.92</td>\n",
       "      <td>24.3</td>\n",
       "      <td>26.4</td>\n",
       "      <td>1.70</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1.22</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>111115</td>\n",
       "      <td>435450</td>\n",
       "      <td>0.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>37.25</td>\n",
       "      <td>26.8</td>\n",
       "      <td>19.1</td>\n",
       "      <td>0.39</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>379994</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>CO</td>\n",
       "      <td>5.03</td>\n",
       "      <td>25.7</td>\n",
       "      <td>23.5</td>\n",
       "      <td>1.24</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.94</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34277</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>CT</td>\n",
       "      <td>3.57</td>\n",
       "      <td>27.6</td>\n",
       "      <td>17.1</td>\n",
       "      <td>1.06</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>460147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>DE</td>\n",
       "      <td>0.90</td>\n",
       "      <td>26.6</td>\n",
       "      <td>23.1</td>\n",
       "      <td>2.89</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1.39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1798</td>\n",
       "      <td>112216</td>\n",
       "      <td>0.016000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>DC</td>\n",
       "      <td>0.60</td>\n",
       "      <td>29.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>2.53</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75080</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>FL</td>\n",
       "      <td>18.80</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.32</td>\n",
       "      <td>133</td>\n",
       "      <td>1</td>\n",
       "      <td>61745</td>\n",
       "      <td>140424</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>GA</td>\n",
       "      <td>9.69</td>\n",
       "      <td>25.9</td>\n",
       "      <td>22.1</td>\n",
       "      <td>0.81</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.58</td>\n",
       "      <td>381</td>\n",
       "      <td>1</td>\n",
       "      <td>462198</td>\n",
       "      <td>1057286</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HI</td>\n",
       "      <td>1.36</td>\n",
       "      <td>26.9</td>\n",
       "      <td>24.9</td>\n",
       "      <td>2.54</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>ID</td>\n",
       "      <td>1.57</td>\n",
       "      <td>23.2</td>\n",
       "      <td>25.8</td>\n",
       "      <td>1.84</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>IL</td>\n",
       "      <td>12.83</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>0.58</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1711951</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>IN</td>\n",
       "      <td>6.48</td>\n",
       "      <td>25.7</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.81</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1350428</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>IA</td>\n",
       "      <td>3.05</td>\n",
       "      <td>25.4</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1.46</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>674913</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>KS</td>\n",
       "      <td>2.85</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.1</td>\n",
       "      <td>1.48</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1.09</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>107206</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>KY</td>\n",
       "      <td>4.34</td>\n",
       "      <td>24.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>1.11</td>\n",
       "      <td>12.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>225483</td>\n",
       "      <td>1155684</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>LA</td>\n",
       "      <td>4.53</td>\n",
       "      <td>25.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1.19</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>331726</td>\n",
       "      <td>708002</td>\n",
       "      <td>0.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>ME</td>\n",
       "      <td>1.33</td>\n",
       "      <td>26.4</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1.40</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>628279</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>MD</td>\n",
       "      <td>5.77</td>\n",
       "      <td>27.3</td>\n",
       "      <td>18.3</td>\n",
       "      <td>1.02</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.69</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>87189</td>\n",
       "      <td>687049</td>\n",
       "      <td>0.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>MA</td>\n",
       "      <td>6.55</td>\n",
       "      <td>28.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0.70</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1231066</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>MI</td>\n",
       "      <td>9.88</td>\n",
       "      <td>26.4</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.69</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>749113</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>5.30</td>\n",
       "      <td>26.3</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.77</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172023</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MS</td>\n",
       "      <td>2.97</td>\n",
       "      <td>25.8</td>\n",
       "      <td>19.3</td>\n",
       "      <td>1.54</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1.01</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>436631</td>\n",
       "      <td>791305</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>MO</td>\n",
       "      <td>5.99</td>\n",
       "      <td>25.6</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.81</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.67</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>114931</td>\n",
       "      <td>1182012</td>\n",
       "      <td>0.097000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>MT</td>\n",
       "      <td>0.99</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.5</td>\n",
       "      <td>2.31</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>NE</td>\n",
       "      <td>1.83</td>\n",
       "      <td>25.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>1.44</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>28841</td>\n",
       "      <td>0.000520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>NH</td>\n",
       "      <td>1.32</td>\n",
       "      <td>26.8</td>\n",
       "      <td>16.7</td>\n",
       "      <td>1.76</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>326073</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>NJ</td>\n",
       "      <td>8.79</td>\n",
       "      <td>27.7</td>\n",
       "      <td>14.8</td>\n",
       "      <td>0.59</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>672035</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>NM</td>\n",
       "      <td>2.06</td>\n",
       "      <td>25.8</td>\n",
       "      <td>20.4</td>\n",
       "      <td>1.90</td>\n",
       "      <td>10.2</td>\n",
       "      <td>1.11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93516</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>19.38</td>\n",
       "      <td>28.4</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.47</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3880735</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>NC</td>\n",
       "      <td>9.54</td>\n",
       "      <td>25.7</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.98</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>331059</td>\n",
       "      <td>992622</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>0.67</td>\n",
       "      <td>25.3</td>\n",
       "      <td>26.7</td>\n",
       "      <td>2.93</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>OH</td>\n",
       "      <td>11.54</td>\n",
       "      <td>26.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>0.61</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2339511</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>3.75</td>\n",
       "      <td>24.4</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1.29</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1.01</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>OR</td>\n",
       "      <td>3.83</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>1.10</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52465</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>PA</td>\n",
       "      <td>12.70</td>\n",
       "      <td>27.1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2906215</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>RI</td>\n",
       "      <td>1.05</td>\n",
       "      <td>28.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.11</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174620</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>SC</td>\n",
       "      <td>4.63</td>\n",
       "      <td>26.4</td>\n",
       "      <td>18.1</td>\n",
       "      <td>1.18</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>402406</td>\n",
       "      <td>703708</td>\n",
       "      <td>0.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>SD</td>\n",
       "      <td>0.81</td>\n",
       "      <td>25.6</td>\n",
       "      <td>20.1</td>\n",
       "      <td>2.64</td>\n",
       "      <td>10.9</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4837</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>TN</td>\n",
       "      <td>6.35</td>\n",
       "      <td>25.2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0.85</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.75</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>275719</td>\n",
       "      <td>1109801</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Texas</td>\n",
       "      <td>TX</td>\n",
       "      <td>25.15</td>\n",
       "      <td>25.2</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0.61</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>182566</td>\n",
       "      <td>604215</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Utah</td>\n",
       "      <td>UT</td>\n",
       "      <td>2.76</td>\n",
       "      <td>23.3</td>\n",
       "      <td>29.6</td>\n",
       "      <td>1.77</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40273</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>VT</td>\n",
       "      <td>0.63</td>\n",
       "      <td>26.9</td>\n",
       "      <td>16.4</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315098</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>VA</td>\n",
       "      <td>8.00</td>\n",
       "      <td>26.4</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.83</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.52</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>490865</td>\n",
       "      <td>1219630</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Washington</td>\n",
       "      <td>WA</td>\n",
       "      <td>6.72</td>\n",
       "      <td>25.9</td>\n",
       "      <td>21.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11594</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>WV</td>\n",
       "      <td>1.85</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>1.69</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1.34</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>18371</td>\n",
       "      <td>376688</td>\n",
       "      <td>0.049000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>WI</td>\n",
       "      <td>5.69</td>\n",
       "      <td>26.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>0.79</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>775881</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>WY</td>\n",
       "      <td>0.56</td>\n",
       "      <td>24.2</td>\n",
       "      <td>30.7</td>\n",
       "      <td>3.92</td>\n",
       "      <td>10.3</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location Loc  Population  MedianAgeMarriage  Marriage  \\\n",
       "0                Alabama  AL        4.78               25.3      20.2   \n",
       "1                 Alaska  AK        0.71               25.2      26.0   \n",
       "2                Arizona  AZ        6.33               25.8      20.3   \n",
       "3               Arkansas  AR        2.92               24.3      26.4   \n",
       "4             California  CA       37.25               26.8      19.1   \n",
       "5               Colorado  CO        5.03               25.7      23.5   \n",
       "6            Connecticut  CT        3.57               27.6      17.1   \n",
       "7               Delaware  DE        0.90               26.6      23.1   \n",
       "8   District of Columbia  DC        0.60               29.7      17.7   \n",
       "9                Florida  FL       18.80               26.4      17.0   \n",
       "10               Georgia  GA        9.69               25.9      22.1   \n",
       "11                Hawaii  HI        1.36               26.9      24.9   \n",
       "12                 Idaho  ID        1.57               23.2      25.8   \n",
       "13              Illinois  IL       12.83               27.0      17.9   \n",
       "14               Indiana  IN        6.48               25.7      19.8   \n",
       "15                  Iowa  IA        3.05               25.4      21.5   \n",
       "16                Kansas  KS        2.85               25.0      22.1   \n",
       "17              Kentucky  KY        4.34               24.8      22.2   \n",
       "18             Louisiana  LA        4.53               25.9      20.6   \n",
       "19                 Maine  ME        1.33               26.4      13.5   \n",
       "20              Maryland  MD        5.77               27.3      18.3   \n",
       "21         Massachusetts  MA        6.55               28.5      15.8   \n",
       "22              Michigan  MI        9.88               26.4      16.5   \n",
       "23             Minnesota  MN        5.30               26.3      15.3   \n",
       "24           Mississippi  MS        2.97               25.8      19.3   \n",
       "25              Missouri  MO        5.99               25.6      18.6   \n",
       "26               Montana  MT        0.99               25.7      18.5   \n",
       "27              Nebraska  NE        1.83               25.4      19.6   \n",
       "28         New Hampshire  NH        1.32               26.8      16.7   \n",
       "29            New Jersey  NJ        8.79               27.7      14.8   \n",
       "30            New Mexico  NM        2.06               25.8      20.4   \n",
       "31              New York  NY       19.38               28.4      16.8   \n",
       "32        North Carolina  NC        9.54               25.7      20.4   \n",
       "33          North Dakota  ND        0.67               25.3      26.7   \n",
       "34                  Ohio  OH       11.54               26.3      16.9   \n",
       "35              Oklahoma  OK        3.75               24.4      23.8   \n",
       "36                Oregon  OR        3.83               26.0      18.9   \n",
       "37          Pennsylvania  PA       12.70               27.1      15.5   \n",
       "38          Rhode Island  RI        1.05               28.2      15.0   \n",
       "39        South Carolina  SC        4.63               26.4      18.1   \n",
       "40          South Dakota  SD        0.81               25.6      20.1   \n",
       "41             Tennessee  TN        6.35               25.2      19.4   \n",
       "42                 Texas  TX       25.15               25.2      21.5   \n",
       "43                  Utah  UT        2.76               23.3      29.6   \n",
       "44               Vermont  VT        0.63               26.9      16.4   \n",
       "45              Virginia  VA        8.00               26.4      20.5   \n",
       "46            Washington  WA        6.72               25.9      21.4   \n",
       "47         West Virginia  WV        1.85               25.0      22.2   \n",
       "48             Wisconsin  WI        5.69               26.3      17.2   \n",
       "49               Wyoming  WY        0.56               24.2      30.7   \n",
       "\n",
       "    Marriage SE  Divorce  Divorce SE  WaffleHouses  South  Slaves1860  \\\n",
       "0          1.27     12.7        0.79           128      1      435080   \n",
       "1          2.93     12.5        2.05             0      0           0   \n",
       "2          0.98     10.8        0.74            18      0           0   \n",
       "3          1.70     13.5        1.22            41      1      111115   \n",
       "4          0.39      8.0        0.24             0      0           0   \n",
       "5          1.24     11.6        0.94            11      0           0   \n",
       "6          1.06      6.7        0.77             0      0           0   \n",
       "7          2.89      8.9        1.39             3      0        1798   \n",
       "8          2.53      6.3        1.89             0      0           0   \n",
       "9          0.58      8.5        0.32           133      1       61745   \n",
       "10         0.81     11.5        0.58           381      1      462198   \n",
       "11         2.54      8.3        1.27             0      0           0   \n",
       "12         1.84      7.7        1.05             0      0           0   \n",
       "13         0.58      8.0        0.45             2      0           0   \n",
       "14         0.81     11.0        0.63            17      0           0   \n",
       "15         1.46     10.2        0.91             0      0           0   \n",
       "16         1.48     10.6        1.09             6      0           2   \n",
       "17         1.11     12.6        0.75            64      1      225483   \n",
       "18         1.19     11.0        0.89            66      1      331726   \n",
       "19         1.40     13.0        1.48             0      0           0   \n",
       "20         1.02      8.8        0.69            11      0       87189   \n",
       "21         0.70      7.8        0.52             0      0           0   \n",
       "22         0.69      9.2        0.53             0      0           0   \n",
       "23         0.77      7.4        0.60             0      0           0   \n",
       "24         1.54     11.1        1.01            72      1      436631   \n",
       "25         0.81      9.5        0.67            39      1      114931   \n",
       "26         2.31      9.1        1.71             0      0           0   \n",
       "27         1.44      8.8        0.94             0      0          15   \n",
       "28         1.76     10.1        1.61             0      0           0   \n",
       "29         0.59      6.1        0.46             0      0          18   \n",
       "30         1.90     10.2        1.11             2      0           0   \n",
       "31         0.47      6.6        0.31             0      0           0   \n",
       "32         0.98      9.9        0.48           142      1      331059   \n",
       "33         2.93      8.0        1.44             0      0           0   \n",
       "34         0.61      9.5        0.45            64      0           0   \n",
       "35         1.29     12.8        1.01            16      0           0   \n",
       "36         1.10     10.4        0.80             0      0           0   \n",
       "37         0.48      7.7        0.43            11      0           0   \n",
       "38         2.11      9.4        1.79             0      0           0   \n",
       "39         1.18      8.1        0.70           144      1      402406   \n",
       "40         2.64     10.9        2.50             0      0           0   \n",
       "41         0.85     11.4        0.75           103      1      275719   \n",
       "42         0.61     10.0        0.35            99      1      182566   \n",
       "43         1.77     10.2        0.93             0      0           0   \n",
       "44         2.40      9.6        1.87             0      0           0   \n",
       "45         0.83      8.9        0.52            40      1      490865   \n",
       "46         1.00     10.0        0.65             0      0           0   \n",
       "47         1.69     10.9        1.34             4      1       18371   \n",
       "48         0.79      8.3        0.57             0      0           0   \n",
       "49         3.92     10.3        1.90             0      0           0   \n",
       "\n",
       "    Population1860  PropSlaves1860  \n",
       "0           964201        0.450000  \n",
       "1                0        0.000000  \n",
       "2                0        0.000000  \n",
       "3           435450        0.260000  \n",
       "4           379994        0.000000  \n",
       "5            34277        0.000000  \n",
       "6           460147        0.000000  \n",
       "7           112216        0.016000  \n",
       "8            75080        0.000000  \n",
       "9           140424        0.440000  \n",
       "10         1057286        0.440000  \n",
       "11               0        0.000000  \n",
       "12               0        0.000000  \n",
       "13         1711951        0.000000  \n",
       "14         1350428        0.000000  \n",
       "15          674913        0.000000  \n",
       "16          107206        0.000019  \n",
       "17         1155684        0.000000  \n",
       "18          708002        0.470000  \n",
       "19          628279        0.000000  \n",
       "20          687049        0.130000  \n",
       "21         1231066        0.000000  \n",
       "22          749113        0.000000  \n",
       "23          172023        0.000000  \n",
       "24          791305        0.550000  \n",
       "25         1182012        0.097000  \n",
       "26               0        0.000000  \n",
       "27           28841        0.000520  \n",
       "28          326073        0.000000  \n",
       "29          672035        0.000027  \n",
       "30           93516        0.000000  \n",
       "31         3880735        0.000000  \n",
       "32          992622        0.330000  \n",
       "33               0        0.000000  \n",
       "34         2339511        0.000000  \n",
       "35               0        0.000000  \n",
       "36           52465        0.000000  \n",
       "37         2906215        0.000000  \n",
       "38          174620        0.000000  \n",
       "39          703708        0.570000  \n",
       "40            4837        0.000000  \n",
       "41         1109801        0.200000  \n",
       "42          604215        0.300000  \n",
       "43           40273        0.000000  \n",
       "44          315098        0.000000  \n",
       "45         1219630        0.400000  \n",
       "46           11594        0.000000  \n",
       "47          376688        0.049000  \n",
       "48          775881        0.000000  \n",
       "49               0        0.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATASET_URL = \"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/WaffleDivorce.csv\"\n",
    "dset = pd.read_csv(DATASET_URL, sep=\";\")\n",
    "dset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hu-1n8UfN7l6"
   },
   "source": [
    "Let us plot the pair-wise relationship amongst the main variables in the dataset, using `seaborn.pairplot`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T17:14:49.947399Z",
     "start_time": "2024-07-29T17:14:47.358302Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "pNjxHdVvN7l6",
    "outputId": "eb113ed4-bc40-45f5-e82d-c56142336227"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vars = [\n",
    "    \"Population\",\n",
    "    \"MedianAgeMarriage\",\n",
    "    \"Marriage\",\n",
    "    \"WaffleHouses\",\n",
    "    \"South\",\n",
    "    \"Divorce\",\n",
    "]\n",
    "sns.pairplot(dset, x_vars=vars, y_vars=vars, palette=\"husl\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nzqje01_N7l7"
   },
   "source": [
    "From the plots above, we can clearly observe that there is a relationship between divorce rates and marriage rates in a state (as might be expected), and also between divorce rates and median age of marriage. \n",
    "\n",
    "There is also a weak relationship between number of Waffle Houses and divorce rates, which is not obvious from the plot above, but will be clearer if we regress `Divorce` against `WaffleHouse` and plot the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T17:12:19.000003Z",
     "start_time": "2024-07-29T17:12:18.887497Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "jBhug-OzN7l8",
    "outputId": "9f95c298-43e7-4f40-cde5-0a9364088980"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"WaffleHouses\", y=\"Divorce\", data=dset);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kc4rautdN7l9"
   },
   "source": [
    "This is an example of a spurious association. We do not expect the number of Waffle Houses in a state to affect the divorce rate, but it is likely correlated with other factors that have an effect on the divorce rate. We will not delve into this spurious association in this tutorial, but the interested reader is encouraged to read Chapters 5 and 6 of [[1](#References)] which explores the problem of causal association in the presence of multiple predictors. \n",
    "\n",
    "For simplicity, we will primarily focus on marriage rate and the median age of marriage as our predictors for divorce rate throughout the remaining tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mQuCSnFLN7l9"
   },
   "source": [
    "## Regression Model to Predict Divorce Rate\n",
    "\n",
    "Let us now write a regression model in *NumPyro* to predict the divorce rate as a linear function of marriage rate and median age of marriage in each of the states. \n",
    "\n",
    "First, note that our predictor variables have somewhat different scales. It is a good practice to standardize our predictors and response variables to mean `0` and standard deviation `1`, which should result in [faster inference](https://mc-stan.org/docs/2_19/stan-users-guide/standardizing-predictors-and-outputs.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T17:16:29.185237Z",
     "start_time": "2024-07-29T17:16:29.181179Z"
    },
    "id": "CPtcs7a6N7l-"
   },
   "outputs": [],
   "source": [
    "def standardize(x):\n",
    "    return (x - x.mean()) / x.std()\n",
    "\n",
    "\n",
    "dset[\"AgeScaled\"] = dset.MedianAgeMarriage.pipe(standardize)\n",
    "dset[\"MarriageScaled\"] = dset.Marriage.pipe(standardize)\n",
    "dset[\"DivorceScaled\"] = dset.Divorce.pipe(standardize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "olBAKjb1N7l-"
   },
   "source": [
    "We write the NumPyro model as follows. While the code should largely be self-explanatory, take note of the following:\n",
    "\n",
    " - In NumPyro, *model* code is any Python callable which can optionally accept additional arguments and keywords. For HMC which we will be using for this tutorial, these arguments and keywords remain static during inference, but we can reuse the same model to generate [predictions](#Posterior-Predictive-Distribution) on new data.\n",
    " - In addition to regular Python statements, the model code also contains primitives like `sample`. These primitives can be interpreted with various side-effects using effect handlers. For more on effect handlers, refer to [[3](#References)], [[4](#References)]. For now, just remember that a `sample` statement makes this a stochastic function that samples some latent parameters from a *prior distribution*. Our goal is to infer the *posterior distribution* of these parameters conditioned on observed data.\n",
    " - The reason why we have kept our predictors as optional keyword arguments is to be able to reuse the same model as we vary the set of predictors. Likewise, the reason why the response variable is optional is that we would like to reuse this model to sample from the posterior predictive distribution. See the [section](#Posterior-Predictive-Distribution) on plotting the posterior predictive distribution, as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T17:16:29.912629Z",
     "start_time": "2024-07-29T17:16:29.910032Z"
    },
    "id": "hVzAKfmnN7l-"
   },
   "outputs": [],
   "source": [
    "def model(marriage=None, age=None, divorce=None):\n",
    "    a = numpyro.sample(\"a\", dist.Normal(0.0, 0.2))\n",
    "    M, A = 0.0, 0.0\n",
    "    if marriage is not None:\n",
    "        bM = numpyro.sample(\"bM\", dist.Normal(0.0, 0.5))\n",
    "        M = bM * marriage\n",
    "    if age is not None:\n",
    "        bA = numpyro.sample(\"bA\", dist.Normal(0.0, 0.5))\n",
    "        A = bA * age\n",
    "    sigma = numpyro.sample(\"sigma\", dist.Exponential(1.0))\n",
    "    mu = a + M + A\n",
    "    numpyro.sample(\"obs\", dist.Normal(mu, sigma), obs=divorce)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tsUGIXf4N7l_"
   },
   "source": [
    "### Model 1: Predictor - Marriage Rate\n",
    "\n",
    "We first try to model the divorce rate as depending on a single variable, marriage rate. As mentioned above, we can use the same `model` code as earlier, but only pass values for `marriage` and `divorce` keyword arguments. We will use the No U-Turn Sampler (see [[5](#References)] for more details on the NUTS algorithm) to run inference on this simple model.\n",
    "\n",
    "The Hamiltonian Monte Carlo (or, the NUTS) implementation in NumPyro takes in a potential energy function. This is the negative log joint density for the model. Therefore, for our model description above, we need to construct a function which given the parameter values returns the potential energy (or negative log joint density). Additionally, the verlet integrator in HMC (or, NUTS) returns sample values simulated using Hamiltonian dynamics in the unconstrained space. As such, continuous variables with bounded support need to be transformed into unconstrained space using bijective transforms. We also need to transform these samples back to their constrained support before returning these values to the user. Thankfully, this is handled on the backend for us, within a convenience class for doing [MCMC inference](https://numpyro.readthedocs.io/en/latest/mcmc.html#numpyro.mcmc.MCMC) that has the following methods:\n",
    "\n",
    "   - `run(...)`: runs warmup, adapts steps size and mass matrix, and does sampling using the sample from the warmup phase.\n",
    "   - `print_summary()`: print diagnostic information like quantiles, effective sample size, and the Gelman-Rubin diagnostic.\n",
    "   - `get_samples()`: gets samples from the posterior distribution.\n",
    "\n",
    "Note the following:\n",
    "\n",
    " - JAX uses functional PRNGs. Unlike other languages / frameworks which maintain a global random state, in JAX, every call to a sampler requires an [explicit PRNGKey](https://github.com/jax-ml/jax#random-numbers-are-different). We will split our initial random seed for subsequent operations, so that we do not accidentally reuse the same seed.\n",
    " - We run inference with the `NUTS` sampler. To run vanilla HMC, we can instead use the [HMC](https://numpyro.readthedocs.io/en/latest/mcmc.html#numpyro.mcmc.HMC) class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T17:16:37.601666Z",
     "start_time": "2024-07-29T17:16:30.682590Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "er7-QqEhN7l_",
    "outputId": "d52e722b-0061-49a1-d9df-d977a311b88d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 3000/3000 [00:05<00:00, 577.68it/s, 5 steps of size 6.75e-01. acc. prob=0.94]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
      "         a      0.00      0.11     -0.00     -0.19      0.17   1674.85      1.00\n",
      "        bM      0.35      0.13      0.35      0.14      0.56   1756.94      1.00\n",
      "     sigma      0.95      0.10      0.94      0.79      1.12   1632.67      1.00\n",
      "\n",
      "Number of divergences: 0\n"
     ]
    }
   ],
   "source": [
    "# Start from this source of randomness. We will split keys for subsequent operations.\n",
    "rng_key = random.PRNGKey(0)\n",
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "\n",
    "# Run NUTS.\n",
    "kernel = NUTS(model)\n",
    "num_samples = 2000\n",
    "mcmc = MCMC(kernel, num_warmup=1000, num_samples=num_samples)\n",
    "mcmc.run(\n",
    "    rng_key_, marriage=dset.MarriageScaled.values, divorce=dset.DivorceScaled.values\n",
    ")\n",
    "mcmc.print_summary()\n",
    "samples_1 = mcmc.get_samples()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0IfifzKHN7mA"
   },
   "source": [
    "#### Posterior Distribution over the Regression Parameters\n",
    "\n",
    "We notice that the progress bar gives us online statistics on the acceptance probability, step size and number of steps taken per sample while running NUTS. In particular, during warmup, we adapt the step size and mass matrix to achieve a certain target acceptance probability which is 0.8, by default. We were able to successfully adapt our step size to achieve this target in the warmup phase.\n",
    "\n",
    "During warmup, the aim is to adapt hyper-parameters such as step size and mass matrix (the HMC algorithm is very sensitive to these hyper-parameters), and to reach the typical set (see [[6](#References)] for more details). If there are any issues in the model specification, the first signal to notice would be low acceptance probabilities or very high number of steps. We use the sample from the end of the warmup phase to seed the MCMC chain (denoted by the second `sample` progress bar) from which we generate the desired number of samples from our target distribution.\n",
    "\n",
    "At the end of inference, NumPyro prints the mean, std and 90% CI values for each of the latent parameters. Note that since we standardized our predictors and response variable, we would expect the intercept to have mean 0, as can be seen here. It also prints other convergence diagnostics on the latent parameters in the model, including [effective sample size](https://numpyro.readthedocs.io/en/latest/diagnostics.html#numpyro.diagnostics.effective_sample_size) and the [gelman rubin diagnostic](https://numpyro.readthedocs.io/en/latest/diagnostics.html#numpyro.diagnostics.gelman_rubin) ($\\hat{R}$). The value for these diagnostics indicates that the chain has converged to the target distribution. In our case, the \"target distribution\" is the posterior distribution over the latent parameters that we are interested in. Note that this is often worth verifying with multiple chains for more complicated models. In the end, `samples_1` is a collection (in our case, a `dict` since `init_samples` was a `dict`) containing samples from the posterior distribution for each of the latent parameters in the model.\n",
    "\n",
    "To look at our regression fit, let us plot the regression line using our posterior estimates for the regression parameters, along with the 90% Credibility Interval (CI). Note that the [hpdi](https://numpyro.readthedocs.io/en/latest/diagnostics.html#numpyro.diagnostics.hpdi) function in NumPyro's diagnostics module can be used to compute CI. In the functions below, note that the collected samples from the posterior are all along the leading axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "UX-DbOtsN7mA",
    "outputId": "163ad2c9-7567-490f-9535-f6834f457c8a"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_regression(x, y_mean, y_hpdi):\n",
    "    # Sort values for plotting by x axis\n",
    "    idx = jnp.argsort(x)\n",
    "    marriage = x[idx]\n",
    "    mean = y_mean[idx]\n",
    "    hpdi = y_hpdi[:, idx]\n",
    "    divorce = dset.DivorceScaled.values[idx]\n",
    "\n",
    "    # Plot\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n",
    "    ax.plot(marriage, mean)\n",
    "    ax.plot(marriage, divorce, \"o\")\n",
    "    ax.fill_between(marriage, hpdi[0], hpdi[1], alpha=0.3, interpolate=True)\n",
    "    return ax\n",
    "\n",
    "\n",
    "# Compute empirical posterior distribution over mu\n",
    "posterior_mu = (\n",
    "    jnp.expand_dims(samples_1[\"a\"], -1)\n",
    "    + jnp.expand_dims(samples_1[\"bM\"], -1) * dset.MarriageScaled.values\n",
    ")\n",
    "\n",
    "mean_mu = jnp.mean(posterior_mu, axis=0)\n",
    "hpdi_mu = hpdi(posterior_mu, 0.9)\n",
    "ax = plot_regression(dset.MarriageScaled.values, mean_mu, hpdi_mu)\n",
    "ax.set(\n",
    "    xlabel=\"Marriage rate\", ylabel=\"Divorce rate\", title=\"Regression line with 90% CI\"\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sndvig_RQi0s"
   },
   "source": [
    "We can see from the plot, that the CI broadens towards the tails where the data is relatively sparse, as can be expected.\n",
    "\n",
    "#### Prior Predictive Distribution\n",
    "\n",
    "Let us check that we have set sensible priors by sampling from the prior predictive distribution. NumPyro provides a handy [Predictive](http://num.pyro.ai/en/latest/utilities.html#numpyro.infer.util.Predictive) utility for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "vSfDasR7Q5Be",
    "outputId": "a11554aa-96b7-4298-9456-d44e0d63d8af"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpyro.infer import Predictive\n",
    "\n",
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "prior_predictive = Predictive(model, num_samples=100)\n",
    "prior_predictions = prior_predictive(rng_key_, marriage=dset.MarriageScaled.values)[\n",
    "    \"obs\"\n",
    "]\n",
    "mean_prior_pred = jnp.mean(prior_predictions, axis=0)\n",
    "hpdi_prior_pred = hpdi(prior_predictions, 0.9)\n",
    "\n",
    "ax = plot_regression(dset.MarriageScaled.values, mean_prior_pred, hpdi_prior_pred)\n",
    "ax.set(xlabel=\"Marriage rate\", ylabel=\"Divorce rate\", title=\"Predictions with 90% CI\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for `Predictive` to work as expected, the response variable of the model (in this case, `divorce`) must be set to `None`.\n",
    "In the code above this is done implicitly by not passing a value for `divorce` to the model in the call to `prior_predictive`, which due to the model definition, sets `divorce=None`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q1ojeyHpN7mB"
   },
   "source": [
    "#### Posterior Predictive Distribution\n",
    "\n",
    "Let us now look at the posterior predictive distribution to see how our predictive distribution looks with respect to the observed divorce rates. To get samples from the posterior predictive distribution, we need to run the model by substituting the latent parameters with samples from the posterior.  Note that by default we generate a single prediction for each sample from the joint posterior distribution, but this can be controlled using the `num_samples` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "XzzsqL7jN7mB",
    "outputId": "00bb95d0-8be6-4686-f9d9-bc8a8891aa2c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Mean Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.016434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.501293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.025105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.600058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>-0.082887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Location  Mean Predictions\n",
       "0     Alabama          0.016434\n",
       "1      Alaska          0.501293\n",
       "2     Arizona          0.025105\n",
       "3    Arkansas          0.600058\n",
       "4  California         -0.082887"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "predictive = Predictive(model, samples_1)\n",
    "predictions = predictive(rng_key_, marriage=dset.MarriageScaled.values)[\"obs\"]\n",
    "df = dset.filter([\"Location\"])\n",
    "df[\"Mean Predictions\"] = jnp.mean(predictions, axis=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n-Sm_5H8N7mC"
   },
   "source": [
    "#### Predictive Utility With Effect Handlers\n",
    "\n",
    "To remove the magic behind `Predictive`, let us see how we can combine [effect handlers](https://numpyro.readthedocs.io/en/latest/handlers.html) with the [vmap](https://github.com/jax-ml/jax#auto-vectorization-with-vmap) JAX primitive to implement our own simplified predictive utility function that can do vectorized predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "gSKilj8dN7mC"
   },
   "outputs": [],
   "source": [
    "def predict(rng_key, post_samples, model, *args, **kwargs):\n",
    "    model = handlers.seed(handlers.condition(model, post_samples), rng_key)\n",
    "    model_trace = handlers.trace(model).get_trace(*args, **kwargs)\n",
    "    return model_trace[\"obs\"][\"value\"]\n",
    "\n",
    "\n",
    "# vectorize predictions via vmap\n",
    "predict_fn = vmap(\n",
    "    lambda rng_key, samples: predict(\n",
    "        rng_key, samples, model, marriage=dset.MarriageScaled.values\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hGB12J3-N7mC"
   },
   "source": [
    "Note the use of the `condition`, `seed` and `trace` effect handlers in the `predict` function.\n",
    "\n",
    " - The `seed` effect-handler is used to wrap a stochastic function with an initial `PRNGKey` seed. When a sample statement inside the model is called, it uses the existing seed to sample from a distribution but this effect-handler also splits the existing key to ensure that future `sample` calls in the model use the newly split key instead. This is to prevent us from having to explicitly pass in a `PRNGKey` to each `sample` statement in the model.\n",
    " - The `condition` effect handler conditions the latent sample sites to certain values. In our case, we are conditioning on values from the posterior distribution returned by MCMC.\n",
    " - The `trace` effect handler runs the model and records the execution trace within an `OrderedDict`. This trace object contains execution metadata that is useful for computing quantities such as the log joint density.\n",
    " \n",
    "It should be clear now that the `predict` function simply runs the model by substituting the latent parameters with samples from the posterior (generated by the `mcmc` function) to generate predictions. Note the use of JAX's auto-vectorization transform called [vmap](https://github.com/jax-ml/jax#auto-vectorization-with-vmap) to vectorize predictions. Note that if we didn't use `vmap`, we would have to use a native for loop which for each sample which is much slower. Each draw from the posterior can be used to get predictions over all the 50 states. When we vectorize this over all the samples from the posterior using `vmap`, we will get a `predictions_1` array of shape `(num_samples, 50)`. We can then compute the mean and 90% CI of these samples to plot the posterior predictive distribution. We note that our mean predictions match those obtained from the `Predictive` utility class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "Hbo0AzmSN7mD",
    "outputId": "9183ecd2-bb3b-473b-e912-09188806573e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Mean Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.016434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.501293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.025105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.600058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>-0.082887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Location  Mean Predictions\n",
       "0     Alabama          0.016434\n",
       "1      Alaska          0.501293\n",
       "2     Arizona          0.025105\n",
       "3    Arkansas          0.600058\n",
       "4  California         -0.082887"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the same key as we used for Predictive - note that the results are identical.\n",
    "\n",
    "predictions_1 = predict_fn(random.split(rng_key_, num_samples), samples_1)\n",
    "\n",
    "mean_pred = jnp.mean(predictions_1, axis=0)\n",
    "df = dset.filter([\"Location\"])\n",
    "df[\"Mean Predictions\"] = mean_pred\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "x57YjUCLN7mD",
    "outputId": "19ed4248-f852-40d1-da61-f06faa6c4cbd"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hpdi_pred = hpdi(predictions_1, 0.9)\n",
    "\n",
    "ax = plot_regression(dset.MarriageScaled.values, mean_pred, hpdi_pred)\n",
    "ax.set(xlabel=\"Marriage rate\", ylabel=\"Divorce rate\", title=\"Predictions with 90% CI\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b5i71NCFN7mE"
   },
   "source": [
    "We have used the same `plot_regression` function as earlier. We notice that our CI for the predictive distribution is much broader as compared to the last plot due to the additional noise introduced by the `sigma` parameter. Most data points lie well within the 90% CI, which indicates a good fit.\n",
    "\n",
    "#### Posterior Predictive Density\n",
    "\n",
    "Likewise, making use of effect-handlers and `vmap`, we can also compute the log likelihood for this model given the dataset, and the log posterior predictive density [[6](#References)] which is given by \n",
    "$$ log \\prod_{i=1}^{n} \\int p(y_i | \\theta) p_{post}(\\theta) d\\theta \n",
    "\\approx \\sum_{i=1}^n log \\frac{\\sum_s p(\\theta^{s})}{S} \\\\\n",
    "= \\sum_{i=1}^n (log \\sum_s p(\\theta^{s}) - log(S))\n",
    "$$.\n",
    "\n",
    "Here, $i$ indexes the observed data points $y$ and $s$ indexes the posterior samples over the latent parameters $\\theta$. If the posterior predictive density for a model has a comparatively high value, it indicates that the observed data-points have higher probability under the given model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "ZpNwpvk_N7mE"
   },
   "outputs": [],
   "source": [
    "def log_likelihood(rng_key, params, model, *args, **kwargs):\n",
    "    model = handlers.condition(model, params)\n",
    "    model_trace = handlers.trace(model).get_trace(*args, **kwargs)\n",
    "    obs_node = model_trace[\"obs\"]\n",
    "    return obs_node[\"fn\"].log_prob(obs_node[\"value\"])\n",
    "\n",
    "\n",
    "def log_pred_density(rng_key, params, model, *args, **kwargs):\n",
    "    n = list(params.values())[0].shape[0]\n",
    "    log_lk_fn = vmap(\n",
    "        lambda rng_key, params: log_likelihood(rng_key, params, model, *args, **kwargs)\n",
    "    )\n",
    "    log_lk_vals = log_lk_fn(random.split(rng_key, n), params)\n",
    "    return (logsumexp(log_lk_vals, 0) - jnp.log(n)).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dXu_hzcDN7mE"
   },
   "source": [
    "Note that NumPyro provides the [log_likelihood](http://num.pyro.ai/en/latest/utilities.html#log-likelihood) utility function that can be used directly for computing `log likelihood` as in the first function for any general model. In this tutorial, we would like to emphasize that there is nothing magical about such utility functions, and you can roll out your own inference utilities using NumPyro's effect handling stack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dZxKq_ETN7mF",
    "outputId": "9574a5f7-56aa-404a-e491-f7dc2a1c6636"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log posterior predictive density: -66.70008087158203\n"
     ]
    }
   ],
   "source": [
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "print(\n",
    "    \"Log posterior predictive density: {}\".format(\n",
    "        log_pred_density(\n",
    "            rng_key_,\n",
    "            samples_1,\n",
    "            model,\n",
    "            marriage=dset.MarriageScaled.values,\n",
    "            divorce=dset.DivorceScaled.values,\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wbxnyQazN7mF"
   },
   "source": [
    "### Model 2: Predictor - Median Age of Marriage\n",
    "\n",
    "We will now model the divorce rate as a function of the median age of marriage. The computations are mostly a reproduction of what we did for Model 1. Notice the following:\n",
    "\n",
    " - Divorce rate is inversely related to the age of marriage. Hence states where the median age of marriage is low will likely have a higher divorce rate.\n",
    " - We get a higher log likelihood as compared to Model 2, indicating that median age of marriage is likely a much better predictor of divorce rate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZBdf-6NEN7mF",
    "outputId": "128e3a0b-1742-44a3-f3d1-43b0cc4c5eef"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 3000/3000 [00:04<00:00, 722.23it/s, 7 steps of size 7.58e-01. acc. prob=0.92]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
      "         a     -0.00      0.10     -0.00     -0.17      0.16   1942.82      1.00\n",
      "        bA     -0.57      0.12     -0.57     -0.75     -0.38   1995.70      1.00\n",
      "     sigma      0.82      0.08      0.82      0.69      0.96   1865.82      1.00\n",
      "\n",
      "Number of divergences: 0\n"
     ]
    }
   ],
   "source": [
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "\n",
    "mcmc.run(rng_key_, age=dset.AgeScaled.values, divorce=dset.DivorceScaled.values)\n",
    "mcmc.print_summary()\n",
    "samples_2 = mcmc.get_samples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "6udy6d7_N7mG",
    "outputId": "28a2e4af-81be-4229-b944-1af4d64ddd98"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "posterior_mu = (\n",
    "    jnp.expand_dims(samples_2[\"a\"], -1)\n",
    "    + jnp.expand_dims(samples_2[\"bA\"], -1) * dset.AgeScaled.values\n",
    ")\n",
    "mean_mu = jnp.mean(posterior_mu, axis=0)\n",
    "hpdi_mu = hpdi(posterior_mu, 0.9)\n",
    "ax = plot_regression(dset.AgeScaled.values, mean_mu, hpdi_mu)\n",
    "ax.set(\n",
    "    xlabel=\"Median marriage age\",\n",
    "    ylabel=\"Divorce rate\",\n",
    "    title=\"Regression line with 90% CI\",\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "DYGHPBueN7mG",
    "outputId": "7646a234-b1ee-44f2-8510-02b76654d582"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "predictions_2 = Predictive(model, samples_2)(rng_key_, age=dset.AgeScaled.values)[\"obs\"]\n",
    "\n",
    "mean_pred = jnp.mean(predictions_2, axis=0)\n",
    "hpdi_pred = hpdi(predictions_2, 0.9)\n",
    "\n",
    "ax = plot_regression(dset.AgeScaled.values, mean_pred, hpdi_pred)\n",
    "ax.set(xlabel=\"Median Age\", ylabel=\"Divorce rate\", title=\"Predictions with 90% CI\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FbKMD13UN7mH",
    "outputId": "b271e24a-b880-4e2c-97e9-15ea3a671610"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log posterior predictive density: -59.251956939697266\n"
     ]
    }
   ],
   "source": [
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "print(\n",
    "    \"Log posterior predictive density: {}\".format(\n",
    "        log_pred_density(\n",
    "            rng_key_,\n",
    "            samples_2,\n",
    "            model,\n",
    "            age=dset.AgeScaled.values,\n",
    "            divorce=dset.DivorceScaled.values,\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xVss4FJNN7mH"
   },
   "source": [
    "### Model 3: Predictor - Marriage Rate and Median Age of Marriage\n",
    "\n",
    "Finally, we will also model divorce rate as depending on both marriage rate as well as the median age of marriage. Note that the model's posterior predictive density is similar to Model 2 which likely indicates that the marginal information from marriage rate in predicting divorce rate is low when the median age of marriage is already known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "18Qm4F2_N7mH",
    "outputId": "2ce7fc1d-48bb-4c7f-bad6-f0b929f3ac6b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 3000/3000 [00:04<00:00, 644.48it/s, 7 steps of size 4.65e-01. acc. prob=0.94]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
      "         a      0.00      0.10      0.00     -0.17      0.16   2007.41      1.00\n",
      "        bA     -0.61      0.16     -0.61     -0.89     -0.37   1225.02      1.00\n",
      "        bM     -0.07      0.16     -0.07     -0.34      0.19   1275.37      1.00\n",
      "     sigma      0.83      0.08      0.82      0.69      0.96   1820.77      1.00\n",
      "\n",
      "Number of divergences: 0\n"
     ]
    }
   ],
   "source": [
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "\n",
    "mcmc.run(\n",
    "    rng_key_,\n",
    "    marriage=dset.MarriageScaled.values,\n",
    "    age=dset.AgeScaled.values,\n",
    "    divorce=dset.DivorceScaled.values,\n",
    ")\n",
    "mcmc.print_summary()\n",
    "samples_3 = mcmc.get_samples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XfW5xgpwN7mI",
    "outputId": "0561ac6d-ae08-4f60-a5a2-13c81f13ce3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log posterior predictive density: -59.06374740600586\n"
     ]
    }
   ],
   "source": [
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "print(\n",
    "    \"Log posterior predictive density: {}\".format(\n",
    "        log_pred_density(\n",
    "            rng_key_,\n",
    "            samples_3,\n",
    "            model,\n",
    "            marriage=dset.MarriageScaled.values,\n",
    "            age=dset.AgeScaled.values,\n",
    "            divorce=dset.DivorceScaled.values,\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nptmx2OaN7mI"
   },
   "source": [
    "### Divorce Rate Residuals by State\n",
    "\n",
    "The regression plots above shows that the observed divorce rates for many states differs considerably from the mean regression line. To dig deeper into how the last model (Model 3) under-predicts or over-predicts for each of the states, we will plot the posterior predictive and residuals (`Observed divorce rate - Predicted divorce rate`) for each of the states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 948
    },
    "id": "3vEDRtFON7mI",
    "outputId": "a11368d2-222d-484d-9529-10ae11cc042a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predictions for Model 3.\n",
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "predictions_3 = Predictive(model, samples_3)(\n",
    "    rng_key_, marriage=dset.MarriageScaled.values, age=dset.AgeScaled.values\n",
    ")[\"obs\"]\n",
    "y = jnp.arange(50)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 16))\n",
    "pred_mean = jnp.mean(predictions_3, axis=0)\n",
    "pred_hpdi = hpdi(predictions_3, 0.9)\n",
    "residuals_3 = dset.DivorceScaled.values - predictions_3\n",
    "residuals_mean = jnp.mean(residuals_3, axis=0)\n",
    "residuals_hpdi = hpdi(residuals_3, 0.9)\n",
    "idx = jnp.argsort(residuals_mean)\n",
    "\n",
    "# Plot posterior predictive\n",
    "ax[0].plot(jnp.zeros(50), y, \"--\")\n",
    "ax[0].errorbar(\n",
    "    pred_mean[idx],\n",
    "    y,\n",
    "    xerr=pred_hpdi[1, idx] - pred_mean[idx],\n",
    "    marker=\"o\",\n",
    "    ms=5,\n",
    "    mew=4,\n",
    "    ls=\"none\",\n",
    "    alpha=0.8,\n",
    ")\n",
    "ax[0].plot(dset.DivorceScaled.values[idx], y, marker=\"o\", ls=\"none\", color=\"gray\")\n",
    "ax[0].set(\n",
    "    xlabel=\"Posterior Predictive (red) vs. Actuals (gray)\",\n",
    "    ylabel=\"State\",\n",
    "    title=\"Posterior Predictive with 90% CI\",\n",
    ")\n",
    "ax[0].set_yticks(y)\n",
    "ax[0].set_yticklabels(dset.Loc.values[idx], fontsize=10)\n",
    "\n",
    "# Plot residuals\n",
    "residuals_3 = dset.DivorceScaled.values - predictions_3\n",
    "residuals_mean = jnp.mean(residuals_3, axis=0)\n",
    "residuals_hpdi = hpdi(residuals_3, 0.9)\n",
    "err = residuals_hpdi[1] - residuals_mean\n",
    "\n",
    "ax[1].plot(jnp.zeros(50), y, \"--\")\n",
    "ax[1].errorbar(\n",
    "    residuals_mean[idx], y, xerr=err[idx], marker=\"o\", ms=5, mew=4, ls=\"none\", alpha=0.8\n",
    ")\n",
    "ax[1].set(xlabel=\"Residuals\", ylabel=\"State\", title=\"Residuals with 90% CI\")\n",
    "ax[1].set_yticks(y)\n",
    "ax[1].set_yticklabels(dset.Loc.values[idx], fontsize=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_gtKVD5jN7mJ"
   },
   "source": [
    "The plot on the left shows the mean predictions with 90% CI for each of the states using Model 3. The gray markers indicate the actual observed divorce rates. The right plot shows the residuals for each of the states, and both these plots are sorted by the residuals, i.e. at the bottom, we are looking at states where the model predictions are higher than the observed rates, whereas at the top, the reverse is true.\n",
    "\n",
    "Overall, the model fit seems good because most observed data points like within a 90% CI around the mean predictions.  However, notice how the model over-predicts by a large margin for states like Idaho (bottom left), and on the other end under-predicts for states like Maine (top right). This is likely indicative of other factors that we are missing out in our model that affect divorce rate across different states. Even ignoring other socio-political variables, one such factor that we have not yet modeled is the measurement noise given by `Divorce SE` in the dataset. We will explore this in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZB2I6FAhN7mJ"
   },
   "source": [
    "## Regression Model with Measurement Error\n",
    "\n",
    "Note that in our previous models, each data point influences the regression line equally. Is this well justified? We will build on the previous model to incorporate measurement error given by `Divorce SE` variable in the dataset. Incorporating measurement noise will be useful in ensuring that observations that have higher confidence (i.e. lower measurement noise) have a greater impact on the regression line. On the other hand, this will also help us better model outliers with high measurement errors. For more details on modeling errors due to measurement noise, refer to Chapter 14 of [[1](#References)].\n",
    "\n",
    "To do this, we will reuse Model 3, with the only change that the final observed value has a measurement error given by `divorce_sd` (notice that this has to be standardized since the `divorce` variable itself has been standardized to mean 0 and std 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "ue9-HyWuN7mJ"
   },
   "outputs": [],
   "source": [
    "def model_se(marriage, age, divorce_sd, divorce=None):\n",
    "    a = numpyro.sample(\"a\", dist.Normal(0.0, 0.2))\n",
    "    bM = numpyro.sample(\"bM\", dist.Normal(0.0, 0.5))\n",
    "    M = bM * marriage\n",
    "    bA = numpyro.sample(\"bA\", dist.Normal(0.0, 0.5))\n",
    "    A = bA * age\n",
    "    sigma = numpyro.sample(\"sigma\", dist.Exponential(1.0))\n",
    "    mu = a + M + A\n",
    "    divorce_rate = numpyro.sample(\"divorce_rate\", dist.Normal(mu, sigma))\n",
    "    numpyro.sample(\"obs\", dist.Normal(divorce_rate, divorce_sd), obs=divorce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "BFm91doZN7mJ"
   },
   "outputs": [],
   "source": [
    "# Standardize\n",
    "dset[\"DivorceScaledSD\"] = dset[\"Divorce SE\"] / jnp.std(dset.Divorce.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "twtxSbBpN7mJ",
    "outputId": "1eb4cd42-3b0d-4ae8-bb65-335b6faf205a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 4000/4000 [00:06<00:00, 578.19it/s, 15 steps of size 2.58e-01. acc. prob=0.93]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                      mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
      "               a     -0.06      0.10     -0.06     -0.20      0.11   3203.01      1.00\n",
      "              bA     -0.61      0.16     -0.61     -0.87     -0.35   2156.51      1.00\n",
      "              bM      0.06      0.17      0.06     -0.21      0.33   1943.15      1.00\n",
      " divorce_rate[0]      1.16      0.36      1.15      0.53      1.72   2488.98      1.00\n",
      " divorce_rate[1]      0.69      0.55      0.68     -0.15      1.65   4832.63      1.00\n",
      " divorce_rate[2]      0.42      0.34      0.42     -0.16      0.96   4419.13      1.00\n",
      " divorce_rate[3]      1.41      0.46      1.40      0.63      2.11   4782.86      1.00\n",
      " divorce_rate[4]     -0.90      0.13     -0.90     -1.12     -0.71   4269.33      1.00\n",
      " divorce_rate[5]      0.65      0.39      0.65      0.01      1.31   4139.51      1.00\n",
      " divorce_rate[6]     -1.36      0.35     -1.36     -1.96     -0.82   5180.21      1.00\n",
      " divorce_rate[7]     -0.33      0.49     -0.33     -1.15      0.45   4089.39      1.00\n",
      " divorce_rate[8]     -1.88      0.59     -1.88     -2.89     -0.93   3305.68      1.00\n",
      " divorce_rate[9]     -0.62      0.17     -0.61     -0.90     -0.34   4936.95      1.00\n",
      "divorce_rate[10]      0.76      0.29      0.76      0.28      1.24   3627.89      1.00\n",
      "divorce_rate[11]     -0.55      0.50     -0.55     -1.38      0.26   3822.80      1.00\n",
      "divorce_rate[12]      0.20      0.53      0.20     -0.74      0.99   1476.70      1.00\n",
      "divorce_rate[13]     -0.86      0.23     -0.87     -1.24     -0.48   5333.10      1.00\n",
      "divorce_rate[14]      0.55      0.30      0.55      0.09      1.05   5533.56      1.00\n",
      "divorce_rate[15]      0.28      0.38      0.28     -0.35      0.92   5179.68      1.00\n",
      "divorce_rate[16]      0.49      0.43      0.49     -0.23      1.16   5134.56      1.00\n",
      "divorce_rate[17]      1.25      0.35      1.24      0.69      1.84   4571.21      1.00\n",
      "divorce_rate[18]      0.42      0.38      0.41     -0.15      1.10   4946.86      1.00\n",
      "divorce_rate[19]      0.38      0.55      0.36     -0.50      1.29   2145.11      1.00\n",
      "divorce_rate[20]     -0.56      0.34     -0.56     -1.12     -0.02   5219.59      1.00\n",
      "divorce_rate[21]     -1.11      0.27     -1.11     -1.53     -0.65   3778.88      1.00\n",
      "divorce_rate[22]     -0.28      0.26     -0.28     -0.71      0.13   5751.65      1.00\n",
      "divorce_rate[23]     -0.99      0.30     -0.99     -1.46     -0.49   4385.57      1.00\n",
      "divorce_rate[24]      0.43      0.41      0.42     -0.26      1.08   3868.84      1.00\n",
      "divorce_rate[25]     -0.03      0.32     -0.03     -0.57      0.48   5927.41      1.00\n",
      "divorce_rate[26]     -0.01      0.49     -0.01     -0.79      0.81   4581.29      1.00\n",
      "divorce_rate[27]     -0.16      0.39     -0.15     -0.79      0.49   4522.45      1.00\n",
      "divorce_rate[28]     -0.27      0.50     -0.29     -1.08      0.53   3824.97      1.00\n",
      "divorce_rate[29]     -1.79      0.24     -1.78     -2.18     -1.39   5134.14      1.00\n",
      "divorce_rate[30]      0.17      0.42      0.16     -0.55      0.82   5978.21      1.00\n",
      "divorce_rate[31]     -1.66      0.16     -1.66     -1.93     -1.41   5976.18      1.00\n",
      "divorce_rate[32]      0.12      0.25      0.12     -0.27      0.52   5759.99      1.00\n",
      "divorce_rate[33]     -0.04      0.52     -0.04     -0.91      0.82   2926.68      1.00\n",
      "divorce_rate[34]     -0.13      0.22     -0.13     -0.50      0.23   4390.05      1.00\n",
      "divorce_rate[35]      1.27      0.43      1.27      0.53      1.94   4659.54      1.00\n",
      "divorce_rate[36]      0.22      0.36      0.22     -0.36      0.84   3758.16      1.00\n",
      "divorce_rate[37]     -1.02      0.23     -1.02     -1.38     -0.64   5954.84      1.00\n",
      "divorce_rate[38]     -0.93      0.54     -0.94     -1.84     -0.06   3289.66      1.00\n",
      "divorce_rate[39]     -0.67      0.33     -0.67     -1.18     -0.09   4787.55      1.00\n",
      "divorce_rate[40]      0.25      0.55      0.24     -0.67      1.16   4526.98      1.00\n",
      "divorce_rate[41]      0.73      0.34      0.73      0.17      1.29   4237.28      1.00\n",
      "divorce_rate[42]      0.20      0.18      0.20     -0.10      0.48   5156.91      1.00\n",
      "divorce_rate[43]      0.81      0.43      0.81      0.14      1.50   2067.24      1.00\n",
      "divorce_rate[44]     -0.42      0.51     -0.43     -1.23      0.45   3844.29      1.00\n",
      "divorce_rate[45]     -0.39      0.25     -0.39     -0.78      0.04   4611.94      1.00\n",
      "divorce_rate[46]      0.13      0.31      0.13     -0.36      0.64   5879.70      1.00\n",
      "divorce_rate[47]      0.56      0.47      0.56     -0.15      1.37   4319.38      1.00\n",
      "divorce_rate[48]     -0.63      0.28     -0.63     -1.11     -0.18   5820.05      1.00\n",
      "divorce_rate[49]      0.86      0.59      0.88     -0.10      1.79   2460.53      1.00\n",
      "           sigma      0.58      0.11      0.57      0.40      0.76    735.02      1.00\n",
      "\n",
      "Number of divergences: 0\n"
     ]
    }
   ],
   "source": [
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "\n",
    "kernel = NUTS(model_se, target_accept_prob=0.9)\n",
    "mcmc = MCMC(kernel, num_warmup=1000, num_samples=3000)\n",
    "mcmc.run(\n",
    "    rng_key_,\n",
    "    marriage=dset.MarriageScaled.values,\n",
    "    age=dset.AgeScaled.values,\n",
    "    divorce_sd=dset.DivorceScaledSD.values,\n",
    "    divorce=dset.DivorceScaled.values,\n",
    ")\n",
    "mcmc.print_summary()\n",
    "samples_4 = mcmc.get_samples()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "puqL7TzPN7mJ"
   },
   "source": [
    "### Effect of Incorporating Measurement Noise on Residuals\n",
    "\n",
    "Notice that our values for the regression coefficients is very similar to Model 3. However, introducing measurement noise allows us to more closely match our predictive distribution to the observed values. We can see this if we plot the residuals as earlier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "XKnLI7__N7mJ"
   },
   "outputs": [],
   "source": [
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "predictions_4 = Predictive(model_se, samples_4)(\n",
    "    rng_key_,\n",
    "    marriage=dset.MarriageScaled.values,\n",
    "    age=dset.AgeScaled.values,\n",
    "    divorce_sd=dset.DivorceScaledSD.values,\n",
    ")[\"obs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 993
    },
    "id": "a7aC5dgdN7mJ",
    "outputId": "e6878a39-dbb8-485c-bd56-79a66155f8a5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd = dset.DivorceScaledSD.values\n",
    "residuals_4 = dset.DivorceScaled.values - predictions_4\n",
    "residuals_mean = jnp.mean(residuals_4, axis=0)\n",
    "residuals_hpdi = hpdi(residuals_4, 0.9)\n",
    "err = residuals_hpdi[1] - residuals_mean\n",
    "idx = jnp.argsort(residuals_mean)\n",
    "y = jnp.arange(50)\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 16))\n",
    "\n",
    "\n",
    "# Plot Residuals\n",
    "ax.plot(jnp.zeros(50), y, \"--\")\n",
    "ax.errorbar(\n",
    "    residuals_mean[idx], y, xerr=err[idx], marker=\"o\", ms=5, mew=4, ls=\"none\", alpha=0.8\n",
    ")\n",
    "\n",
    "# Plot SD\n",
    "ax.errorbar(residuals_mean[idx], y, xerr=sd[idx], ls=\"none\", color=\"orange\", alpha=0.9)\n",
    "\n",
    "# Plot earlier mean residual\n",
    "ax.plot(\n",
    "    jnp.mean(dset.DivorceScaled.values - predictions_3, 0)[idx],\n",
    "    y,\n",
    "    ls=\"none\",\n",
    "    marker=\"o\",\n",
    "    ms=6,\n",
    "    color=\"black\",\n",
    "    alpha=0.6,\n",
    ")\n",
    "\n",
    "ax.set(xlabel=\"Residuals\", ylabel=\"State\", title=\"Residuals with 90% CI\")\n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels(dset.Loc.values[idx], fontsize=10)\n",
    "ax.text(\n",
    "    -2.8,\n",
    "    -7,\n",
    "    \"Residuals (with error-bars) from current model (in red). \"\n",
    "    \"Black marker \\nshows residuals from the previous model (Model 3). \"\n",
    "    \"Measurement \\nerror is indicated by orange bar.\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "05vX0drHN7mK"
   },
   "source": [
    "The plot above shows the residuals for each of the states, along with the measurement noise given by inner error bar. The gray dots are the mean residuals from our earlier Model 3. Notice how having an additional degree of freedom to model the measurement noise has shrunk the residuals. In particular, for Idaho and Maine, our predictions are now much closer to the observed values after incorporating measurement noise in the model.\n",
    "\n",
    "To better see how measurement noise affects the movement of the regression line, let us plot the residuals with respect to the measurement noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "lmvXOBAsN7mK",
    "outputId": "12aba4b5-d974-4887-8be1-b223f73d0ad0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 6))\n",
    "x = dset.DivorceScaledSD.values\n",
    "y1 = jnp.mean(residuals_3, 0)\n",
    "y2 = jnp.mean(residuals_4, 0)\n",
    "ax.plot(x, y1, ls=\"none\", marker=\"o\")\n",
    "ax.plot(x, y2, ls=\"none\", marker=\"o\")\n",
    "for i, (j, k) in enumerate(zip(y1, y2)):\n",
    "    ax.plot([x[i], x[i]], [j, k], \"--\", color=\"gray\")\n",
    "\n",
    "ax.set(\n",
    "    xlabel=\"Measurement Noise\",\n",
    "    ylabel=\"Residual\",\n",
    "    title=\"Mean residuals (Model 4: red, Model 3: blue)\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YzFlMShkN7mL"
   },
   "source": [
    "The plot above shows what has happend in more detail - the regression line itself has moved to ensure a better fit for observations with low measurement noise (left of the plot) where the residuals have shrunk very close to 0. That is to say that data points with low measurement error have a concomitantly higher contribution in determining the regression line. On the other hand, for states with high measurement error (right of the plot), incorporating measurement noise allows us to move our posterior distribution mass closer to the observations resulting in a shrinkage of residuals as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recording the number of gradient evaluations used by NUTS\n",
    "\n",
    "Hamiltonian Monte Carlo (and by extension NUTS) work by computing a potential function (which represents how unlikely it is for a set of parameters to give rise to the data) and repeatedly evaluating the gradient of this potential function as the Monte Carlo run progresses. In most cases, the evaluation of this gradient is (by far) the most expensive part of the algorithm, so it is useful to know how many times this gradient has been evaluated. This can be done by setting `extra_fields=\"num_steps\"` when calling `mcmc.run`.\n",
    "\n",
    "To count the number of gradient evaluations used in the warmup phase as well, we must also set `collect_warmup=True` when calling `mcmc.warmup`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T17:21:58.370279Z",
     "start_time": "2024-07-29T17:21:51.785260Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "warmup: 100%|██████████| 1000/1000 [00:02<00:00, 407.77it/s, 1 steps of size 6.75e-01. acc. prob=0.79]\n",
      "sample: 100%|██████████| 2000/2000 [00:03<00:00, 508.24it/s, 7 steps of size 6.75e-01. acc. prob=0.94]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of warmup steps: 4529.  Total number of gradient evaluations: 15611\n"
     ]
    }
   ],
   "source": [
    "rng_key = random.PRNGKey(0)\n",
    "rng_key, rng_key_ = random.split(rng_key)\n",
    "\n",
    "# Run NUTS.\n",
    "kernel = NUTS(model)\n",
    "num_samples = 2000\n",
    "mcmc = MCMC(kernel, num_warmup=1000, num_samples=num_samples)\n",
    "\n",
    "# First do the warmup, so we count the number of warmup steps as well.\n",
    "# Do not forget to set `collect_warmup=True`!\n",
    "mcmc.warmup(\n",
    "    rng_key_,\n",
    "    marriage=dset.MarriageScaled.values,\n",
    "    divorce=dset.DivorceScaled.values,\n",
    "    collect_warmup=True,\n",
    "    extra_fields=(\"num_steps\",),\n",
    ")\n",
    "warmup_steps = sum(mcmc.get_extra_fields()[\"num_steps\"])\n",
    "\n",
    "mcmc.run(\n",
    "    rng_key_,\n",
    "    marriage=dset.MarriageScaled.values,\n",
    "    divorce=dset.DivorceScaled.values,\n",
    "    extra_fields=(\"num_steps\",),\n",
    ")\n",
    "total_steps = sum(mcmc.get_extra_fields()[\"num_steps\"]) + warmup_steps\n",
    "\n",
    "print(\n",
    "    f\"Number of warmup steps: {warmup_steps}.  Total number of gradient evaluations: {total_steps}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1NmiOj_fN7mL"
   },
   "source": [
    "## References\n",
    "\n",
    "1. McElreath, R. (2016). Statistical Rethinking: A Bayesian Course with Examples in R and Stan CRC Press.\n",
    "2. Stan Development Team. [Stan User's Guide](https://mc-stan.org/docs/2_19/stan-users-guide/index.html)\n",
    "3. Goodman, N.D., and StuhlMueller, A. (2014). [The Design and Implementation of Probabilistic Programming Languages](http://dippl.org/)\n",
    "4. Pyro Development Team. [Poutine: A Guide to Programming with Effect Handlers in Pyro](http://pyro.ai/examples/effect_handlers.html)\n",
    "5. Hoffman, M.D., Gelman, A. (2011). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.\n",
    "6. Betancourt, M. (2017). A Conceptual Introduction to Hamiltonian Monte Carlo.\n",
    "7. JAX Development Team (2018). [Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more](https://github.com/jax-ml/jax)\n",
    "8. Gelman, A., Hwang, J., and Vehtari A. [Understanding predictive information criteria for Bayesian models](https://arxiv.org/pdf/1307.5928.pdf)"
   ]
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